I have gone down this route but I'm afraid that 16K of context is nothing for agentic coding tasks. Even if you use a smarter model directly from an inference provider to plan all the work and then use your own 16GB GPU to execute the plan, managing those 16k tokens becomes unusable at some point even with a minimalistic harness like pi. For other agentic tasks it's nice, and more than just the budget, it's the data sovereignty that you gain (imagine analysing tax forms, contracts, other legal documents, etc.) in my opinion.

Thanks for writing and sharing. Since I also have a 4070 Ti Super, I'm always interested in hearing people's experiences with local models that fit 'middle-ground' hardware. I kind of feel left out because most posts I see seem to either be about clever ways of making older, lower-end cards usable, leveraging mega-CPU RAM (128GB) or how aweseome high-end GPUs like 4090/5090 can be.

I use the free versions on mobile, and openrouter on desktop. It’s way cheaper than paying a subscription, and I can always use the latest models.

It's two different things to be honest.

Local model can be a local assistant, take care of your todos, calendar, private stuff in general. Things you shouldn't share with OpenAI or other Big Tech. Big Tech models for anything else.

> llama.cpp serves it over localhost at speeds that stop being a complaint after a few minutes of use.

"That stop being a complaint"? That's a strange construction.

I wonder if qwen 4 will make these smaller cards more viable by allowing the ngram storage to be hosted on CPU RAM.

Ok. What can I do with an M4 MBP, with 48G memory?

If you were only paying $20 for LLMs previously then you were getting very low usage / little done.

20$/month vs. 1200$ gpu. That's a lot of months you can pay the subscription

This is a great article! Thank you for sharing. I also recommend opencode that has built in features for local model hosting.

"I spent $1100 on a graphics card so I can save $20/mo running a mid/low intelligence model at 33tk/s with 16k context"